class_docstr: A posterior based on GPyTorch's multi-variate Normal distributions.
docstr: The posterior mean.
docstr_tok: ["The","posterior","mean","."]
code: def mean(self) -> Tensor:
      r"""The posterior mean."""
      mean = self.distribution.mean
      if not self._is_mt:
          mean = mean.unsqueeze(-1)
      return mean
  
imports: from __future__ import annotations
  import warnings
  from contextlib import ExitStack
  from typing import Optional, Tuple, TYPE_CHECKING, Union
  import torch
  from botorch.exceptions.errors import BotorchTensorDimensionError
  from botorch.posteriors.base_samples import _reshape_base_samples_non_interleaved
  from botorch.posteriors.torch import TorchPosterior
  from gpytorch.distributions import MultitaskMultivariateNormal, MultivariateNormal
  from linear_operator import settings
  from linear_operator.operators import BlockDiagLinearOperator, DenseLinearOperator, LinearOperator, SumLinearOperator
  from torch import Tensor
  from torch.distributions import Normal
label_desc: Use image node_id 8 for calling the GPyTorchPosterior obj's underlying member method code with example usage: obj.mean() and returns: mean